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import json |
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import datasets |
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import pandas as pd |
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from huggingface_hub.file_download import hf_hub_url |
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try: |
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import lzma as xz |
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except ImportError: |
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import pylzma as xz |
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datasets.logging.set_verbosity_info() |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION ="""\ |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "" |
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_CITATION = "" |
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_URL = { |
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'data/' |
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} |
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_LANGUAGES = [ |
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"fr","it","es","en","de","pt" |
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] |
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_TYPES = [ |
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"laws", "judgements" |
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] |
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_SOURCES = [ |
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"MultiLegalPile", "Wipolex", "Jug", "BVA", "CC", "IP", "SCOTUS", "SwissJudgementPrediction" |
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"Gesetz", "Constitution", "CivilCode", "CriminalCode", |
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] |
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_HIGHEST_NUMBER_OF_SHARDS = 4 |
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class MultiLegalSBDConfig(datasets.BuilderConfig): |
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def __init__(self, name:str, **kwargs): |
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super( MultiLegalSBDConfig, self).__init__(**kwargs) |
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self.name = name |
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self.language = name.split("_")[0] |
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self.type = name.split("_")[1] |
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class MultiLegalSBD(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIG_CLASS = MultiLegalSBDConfig |
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BUILDER_CONFIGS = [ |
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MultiLegalSBDConfig(f"{language}_{type}") |
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for language in _LANGUAGES + ['all'] |
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for type in _TYPES + ["all"] |
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] |
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DEFAULT_CONFIG_NAME = 'all_all' |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"spans": [ |
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{ |
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"start": datasets.Value("int64"), |
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"end": datasets.Value("int64"), |
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"label": datasets.Value("string"), |
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"token_start": datasets.Value("int64"), |
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"token_end": datasets.Value("int64") |
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} |
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], |
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"tokens": [ |
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{ |
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"text": datasets.Value("string"), |
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"start": datasets.Value("int64"), |
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"end": datasets.Value("int64"), |
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"id": datasets.Value("int64"), |
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"ws": datasets.Value("bool") |
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} |
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], |
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"source": datasets.Value("string") |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features = features, |
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homepage = _HOMEPAGE, |
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citation=_CITATION |
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) |
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def _split_generators(self, dl_manager): |
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def download_url(filename): |
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url = hf_hub_url( |
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repo_id="tbrugger/Multilingual-SBD", |
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filename = f'data/{filename}.jsonl.xz', |
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repo_type='dataset' |
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) |
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return dl_manager.download(url) |
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languages = _LANGUAGES if self.config.language == "all" else [self.config.language] |
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types = _TYPES if self.config.type == 'all' else [self.config.type] |
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split_generators = [] |
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for split in [datasets.Split.TRAIN]: |
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filepaths = [] |
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for language in languages: |
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for type in types: |
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for shard in range(_HIGHEST_NUMBER_OF_SHARDS): |
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try: |
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filepaths.append(download_url(f'{language}_{type}_{shard}')) |
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except: |
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break |
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split_generators.append( |
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datasets.SplitGenerator(name=split, gen_kwargs={'filepaths': filepaths}) |
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) |
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return split_generators |
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def _generate_examples(self,filepaths): |
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id_ = 0 |
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for filepath in filepaths: |
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if filepath: |
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logger.info("Generating examples from = %s", filepath) |
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try: |
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with xz.open(open(filepath,'rb'), 'rt', encoding='utf-8') as f: |
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json_list = list(f) |
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for json_str in json_list: |
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example = json.loads(json_str) |
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if example is not None and isinstance(example, dict): |
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yield id_, example |
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id_ +=1 |
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except Exception: |
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logger.exception("Error while processing file %s", filepath) |
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